{"id":110040,"date":"2025-08-02T13:47:25","date_gmt":"2025-08-02T11:47:25","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=110040"},"modified":"2025-08-12T13:34:25","modified_gmt":"2025-08-12T11:34:25","slug":"analysis-predictive-analytics-scm","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/analysis-predictive-analytics-scm\/","title":{"rendered":"Requirements Analysis for Predictive Analytics in SCM"},"content":{"rendered":"

<\/p>\n

In most industries, supply chains have been and are disrupted from time to time for various reasons (i.e., natural disasters, political turbulence). This situation impacts global value creation [1] and causes stressful situations related to global shipping. Decision problems, for example in supply chain planning, thus become more challenging. Novel technical artifacts could help. As an advanced technology, predictive analytics is increasingly used in different management fields, including in SCM [2]. Predictive analytics as a game changer in SCM Business processes and organizations …<\/div>\n

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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n✓<\/span><\/td>\n−<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n−<\/td>\n
\n \t \n
\t\t

Digital Factory Planning for Startups<\/h4>\n
A simulation-based production structure design<\/div>
Herwig Winkler<\/a> \n \"ORCID<\/a>, Tobias Isau<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n With the increasing complexity of production and logistics systems, traditional factory planning approaches are reaching their limits. In this context, digital factory planning offers a promising solution for enabling well-informed decisions, particularly during the early planning phases. For startups, the optimal planning of a production facility is challenging, as they often operate with limited financial and infrastructural resources. This paper presents a methodological approach to digital factory planning that utilizes VR simulation for the layout planning of a factory hall for a young company in the solar industry. The proposed approach demonstrates how simulations can support the design of flexible production structures, particularly in startup environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 68-75<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Site\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Site Assessment for Flexible Intralogistics in Brownfield Sites<\/h4>\n
Innovative decision support in practice<\/div>
Jolanda Schierbaum<\/a>, Carsten Feldmann<\/a>, Lars Renhof<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Due to its dynamic environment, space planning in intralogistics is not a one-time task but a recurring decision-making process subject to numerous constraints imposed by existing infrastructure. Decisions are often based on incomplete data, resulting in a high risk of poor planning decisions and inefficient use of space. This paper presents a practice-oriented process model for space evaluation in brownfield projects. The proposed approach improves the standardization and consistency of space evaluation and promotes best practices among all stakeholders. By supporting systematic decision-making, the process model contributes to optimized planning and resource allocation, thereby reducing risks and avoiding costly implementation errors.The process model is demonstrated through a case study conducted at a commercial vehicle manufacturer. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 124-133<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Experiencing\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Jan Schickram<\/a>, Tareq Albeesh<\/a>, Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Loam\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Loam Construction and Wooden Shelving<\/h4>\n
A contribution to sustainability in warehouse logistics<\/div>
Viviano De Giacomo<\/a> \n \"ORCID<\/a>, Nathalie Fritsch<\/a> \n \"ORCID<\/a>, Jakob Kennert<\/a> \n \"ORCID<\/a>, Dieter Uckelmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>This study examines the contribution of natural building materials, in particular loam and wood, to the sustainable development of logistics infrastructure, assessing ecological, economic, and technical dimensions across the entire life cycle. Potentials, restrictions, and supportive framework conditions are identified based on literature analyses and expert interviews. Wood proves to be technically mature and ecologically advantageous, especially in high rack construction, while loam offers high potential for energy- and resource-efficient construction. The study concludes with recommendations for research, policy, and practice to establish circular construction methods in logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 82-89<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Electric\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Electric Trucks in Intermodal Terminal Pre- and Post-Carriage<\/h4>\n
Impact on terminal processes in combined road-rail freight transport<\/div>
Ralf Elbert<\/a> \n \"ORCID<\/a>, Samira Ghaneian Sebdani<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Electric trucks (e-trucks) play an important role in reducing CO\u2082 emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 70-77<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Assistance\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Assistance for Simulation in Production and Logistics<\/h4>\n
A literature-based classification<\/div>
Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Felix \u00d6zkul<\/a>, Robin Sutherland<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Despite the commercial availability of simulation tools, using of discrete-event simulation for complex production and logistics systems is becoming increasingly challenging. It requires extensive expertise, high data quality, and considerable time and financial resources. For many years, therefore, there has been high demand for methodological and organizational support for the conduction of simulation studies. This article is based on an analysis of relevant publications and aims to classify previous research on improving the use of simulation. It also raises the question of the need for assistance in applying discrete event simulation and identifies areas for action. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 66-76 | DOI 10.30844\/I4SE.25.5.64<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Predictive analytics opens up opportunities to improve decision-making in manifold areas, including in supply chain management (SCM). Yet, the complete realization of its potential requires the identification of the corresponding needs upfront. This paper provides a structured concept that guides through the complex and interdisciplinary endeavor of requirements analysis for predictive analytics in SCM. Due to the generic nature of this approach, it can be applied for any use case and be adapted or enhanced in case of need.<\/p>\n","protected":false},"featured_media":109620,"menu_order":0,"template":"","categories":[],"tags":[80137,80138],"product_cat":[79304],"topic":[79371],"technology":[67599],"knowhow":[],"industry":[],"writer":[83211],"content-type":[83932],"potential":[68057],"solution":[67610,78673],"glossary":[],"class_list":["post-110040","article","type-article","status-publish","has-post-thumbnail","tag-scor-en","tag-supply-chain-management-en","product_cat-articles","topic-logistics","technology-analytics-en","writer-iris-hausladen-en","content-type-article","potential-management-en","solution-logistik-en","solution-logistics-technology","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581.jpeg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-150x150.jpeg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-666x375.jpeg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-768x432.jpeg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-1024x576.jpeg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-1032x320.jpeg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-764x376.jpeg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-392x320.jpeg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-608x496.jpeg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-640x325.jpeg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-274x376.jpeg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-514x292.jpeg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-320x440.jpeg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-514x289.jpeg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-196x180.jpeg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581.jpeg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581.jpeg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-510x510.jpeg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-510x287.jpeg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-100x100.jpeg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/08\/hausladen-AdobeStock_241360581-64x36.jpeg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Predictive analytics opens up opportunities to improve decision-making in manifold areas, including in supply chain management (SCM). Yet, the complete realization of its potential requires the identification of the corresponding needs upfront. This paper provides a structured concept that guides through the complex and interdisciplinary endeavor of requirements analysis for predictive analytics in SCM. Due…","_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article\/110040","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/article"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/article"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/109620"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=110040"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/categories?post=110040"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/tags?post=110040"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/product_cat?post=110040"},{"taxonomy":"topic","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/topic?post=110040"},{"taxonomy":"technology","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/technology?post=110040"},{"taxonomy":"knowhow","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/knowhow?post=110040"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/industry?post=110040"},{"taxonomy":"writer","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/writer?post=110040"},{"taxonomy":"content-type","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/content-type?post=110040"},{"taxonomy":"potential","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/potential?post=110040"},{"taxonomy":"solution","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/solution?post=110040"},{"taxonomy":"glossary","embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/glossary?post=110040"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}